Feasibility of spectral CT imaging for the detection of liver lesions with gold-based contrast agents – A simulation study

Müllner, M.; Schlattl, Helmut; Hoeschen, Christoph; Dietrich, Olaf

doi:10.1016/j.ejmp.2015.06.004

Cited by 28 publications

(16 citation statements)

References 24 publications

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“…Photon-counting CT has the potential to exploit high-atomic-number elements other than iodine, barium, and gadolinium as contrast agents. Examples include gold and platinum (86,87); xenon, bismuth, lutetium, tungsten, silver, and ytterbium could also be evaluated for use in photon-counting CT (88). Chen et al (55) showed that, with similar molar concentrations of gadolinium and iodine, the signal (in terms of squared signal difference-to-noise ratio normalized by skin dose) with gadolinium is three to 10 times higher than that with iodine.…”

Section: Contrast Agentsmentioning

confidence: 99%

Photon-counting CT: Technical Principles and Clinical Prospects

et al. 2018

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Photon-counting CT is an emerging technology with the potential to dramatically change clinical CT. Photon-counting CT uses new energy-resolving x-ray detectors, with mechanisms that differ substantially from those of conventional energy-integrating detectors. Photon-counting CT detectors count the number of incoming photons and measure photon energy. This technique results in higher contrast-to-noise ratio, improved spatial resolution, and optimized spectral imaging. Photon-counting CT can reduce radiation exposure, reconstruct images at a higher resolution, correct beam-hardening artifacts, optimize the use of contrast agents, and create opportunities for quantitative imaging relative to current CT technology. In this review, the authors will explain the technical principles of photon-counting CT in nonmathematical terms for radiologists and clinicians. Following a general overview of the current status of photon-counting CT, they will explain potential clinical applications of this technology.

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Section: Contrast Agentsmentioning

confidence: 99%

Photon-counting CT: Technical Principles and Clinical Prospects

et al. 2018

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“…1B ). Gold is the element that has been most commonly used as a contrast generating material for SPCCT 6 , 18 , 46 , therefore we included it in this panel despite its relatively high material cost. We herein report the evaluation of the contrast production of these elements using a prototype SPCCT system with a high count rate high enough to cope with photon fluxes needed for medical imaging.…”

Section: Introductionmentioning

confidence: 99%

Assessment of candidate elements for development of spectral photon-counting CT specific contrast agents

Kim

Bar-Ness

Si‐Mohamed

et al. 2018

Sci Rep

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Spectral photon-counting computed tomography (SPCCT) is a rapidly emerging imaging modality that provides energy-dependent information on individual x-ray photons, leading to accurate material decomposition and simultaneous quantification of multiple contrast generating materials. Development of SPCCT-specific contrast agents is needed to overcome the issues with currently used iodinated contrast agents, such as difficulty in differentiation from calcified structures, and yield SPCCT’s full promise. In this study, the contrast generation of different elements is investigated using a prototype SPCCT scanner based on a modified clinical CT system and suitable elements for novel contrast agent development for SPCCT imaging are identified. Furthermore, nanoparticles were synthesized from tantalum as a proof of concept spectral photon-counting CT agent and tested for their in vitro cytotoxicity and contrast generation to provide insight into the feasibility of nanoparticle contrast agent development from these elements. We found that gadolinium, ytterbium and tantalum generate high contrast in spectral photon-counting CT imaging and may be suitable elements for contrast agent development for this modality. Our proof of concept results with tantalum-based nanoparticles underscore this conclusion due to their detectability with spectral photon-counting CT, as well as their biocompatibility.

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“…9 K-edge imaging, or the detection and quantification of contrast agents with heavy elements, is made possible with photon-counting detectors due to their ability to acquire more than two spectral measurements and the ability to tune the energy bin content. Nanoparticles with gold, iodine, or gadolinium have been investigated for tumor detection, 10,11 cardiovascular imaging, 12,13 and theranostic applications. 14 Material decomposition from photon-counting CT data can be performed from either the acquired spectral projection measurements or from the reconstructed spectral images.…”

Section: Introductionmentioning

confidence: 99%

Experimental investigation of neural network estimator and transfer learning techniques for K‐edge spectral CT imaging

et al. 2020

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Purpose Spectral computed tomography (CT) material decomposition algorithms require accurate physics‐based models or empirically derived models. This study investigates a machine learning algorithm and transfer learning techniques for Spectral CT imaging of K‐edge contrast agents using simulated and experimental measurements. Methods A feed forward multilayer perceptron was implemented and trained on data acquired using a step wedge phantom containing acrylic, aluminum, and gadolinium materials. The neural network estimator was evaluated by scanning a rod phantom with varying dilutions of gadolinium oxide nanoparticles and by scanning a rat leg specimen with injected nanoparticles on a bench‐top photon‐counting computed tomography system. The algorithm decomposed each spectral projection measurement into path lengths of acrylic and aluminum and mass lengths of gadolinium. Each basis material sinogram was reconstructed into basis material images using filtered backprojection. Machine learning techniques of data standardization, transfer learning from aggregated pixel data, and transfer learning from simulations were investigated to improve image quality. The algorithm was compared to a previously published empirical method based on a linear approximation and calibration error look‐up tables. Results The combined transfer learning techniques did not improve quantification in the rod phantom and provided only a small qualitative improvement in ring artifacts. Transfer learning from aggregated pixel data and from simulations improved the qualitative image quality of the rat specimen, for which the calibration data were limited. Transfer learning from aggregated pixel data and simulations estimated 3.26, 6.26, and 12.45 mg/mL Gd concentrations compared to true 2.72, 5.44, and 10.88 mg/mL concentrations in the rod phantom. Additionally, the neural networks were able to separate the soft tissue, bone, and gadolinium nanoparticles of the ex vivo rat leg specimen into the different basis images. Conclusions The results demonstrate that empirical K‐edge imaging from calibration measurements using machine learning and transfer learning is possible without explicit models of material attenuations, incident spectra, or the detector response.

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Feasibility of spectral CT imaging for the detection of liver lesions with gold-based contrast agents – A simulation study

Cited by 28 publications

References 24 publications

Photon-counting CT: Technical Principles and Clinical Prospects

Photon-counting CT: Technical Principles and Clinical Prospects

Assessment of candidate elements for development of spectral photon-counting CT specific contrast agents

Experimental investigation of neural network estimator and transfer learning techniques for K‐edge spectral CT imaging

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